{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'0.22.2.post1'"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import sklearn\n",
    "sklearn.__version__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.pyplot import figure\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib\n",
    "# 解决matplotlib不中文显示为方块的问题\n",
    "matplotlib.use('qt4agg')\n",
    "#指定默认字体\n",
    "matplotlib.rcParams['font.sans-serif'] = ['SimHei']\n",
    "matplotlib.rcParams['font.family']='sans-serif' #解决负号'-'显示为方块的问题\n",
    "matplotlib.rcParams['axes.unicode_minus'] = False\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 重复性设置\n",
    "seed = 2020"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "如果特征特别多，可以考虑降维或者特征选择\n",
    "并不是每个特征模型的贡献都是相同的，特征选择让我们只取出那些对模型效果影响较大的特征"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 加载数据"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "创建一个二分类数据集，共有10000个样本，每个样本有50个特征，"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.datasets import make_classification\n",
    "X, y = make_classification(n_samples=10000, n_features=50, n_classes=2, random_state=seed)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=seed)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 评价指标"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import f1_score\n",
    "from sklearn.metrics import accuracy_score\n",
    "def get_socre(y_true, y_pred):\n",
    "    print(\"[+]F1 score : \", f1_score(y_true, y_pred))\n",
    "    print(\"[+]acc score: \", accuracy_score(y_true, y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 全特征训练"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[+]F1 score :  0.9185763042418332\n",
      "[+]acc score:  0.9165\n"
     ]
    }
   ],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "clf = LogisticRegression(random_state=seed)\n",
    "clf.fit(X_train, y_train)\n",
    "get_socre(y_test, clf.predict(X_test))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 特征选择"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "下面的方法只适应于分类任务"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 卡方检验\n",
    "**注意事项：特征数值必须非负**\n",
    "\n",
    "卡方检验衡量随机变量之间的依赖性，可以剔除对分类没有太大帮助的特征\n",
    "\n",
    "参考文章：https://zhuanlan.zhihu.com/p/69888032"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "没有特征选择\n",
      "[+]F1 score :  0.9349593495934959\n",
      "[+]acc score:  0.9053254437869822\n",
      "保留15个重要特征\n",
      "[+]F1 score :  0.9477911646586346\n",
      "[+]acc score:  0.9230769230769231\n",
      "保留20个重要特征\n",
      "[+]F1 score :  0.9561752988047809\n",
      "[+]acc score:  0.9349112426035503\n"
     ]
    }
   ],
   "source": [
    "\"\"\"\n",
    "特征必须非负，我们这里加载另一个数据集\n",
    "\"\"\"\n",
    "from sklearn.datasets import load_breast_cancer\n",
    "from sklearn.feature_selection import SelectKBest\n",
    "from sklearn.feature_selection import chi2\n",
    "\n",
    "dataset = load_breast_cancer()\n",
    "\n",
    "# 特征选择之前\n",
    "print(\"没有特征选择\")\n",
    "clf = LogisticRegression(random_state=seed)\n",
    "clf.fit(dataset.data[:400], dataset.target[:400])\n",
    "get_socre(dataset.target[400:], clf.predict(dataset.data[400:]))\n",
    "\n",
    "# 特征选择之后\n",
    "print(\"保留15个重要特征\")\n",
    "new_data = SelectKBest(chi2, k=15).fit_transform(dataset.data, dataset.target)  # 保留15个重要的特征\n",
    "clf = LogisticRegression(random_state=seed)\n",
    "clf.fit(new_data[:400], dataset.target[:400])\n",
    "get_socre(dataset.target[400:], clf.predict(new_data[400:]))\n",
    "\n",
    "print(\"保留20个重要特征\")\n",
    "new_data = SelectKBest(chi2, k=20).fit_transform(dataset.data, dataset.target)  # 保留20个重要的特征\n",
    "clf = LogisticRegression(random_state=seed)\n",
    "clf.fit(new_data[:400], dataset.target[:400])\n",
    "get_socre(dataset.target[400:], clf.predict(new_data[400:]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 方差分析"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "类似卡方检验，方差分析过分析研究不同来源的变异对总变异的贡献大小，从而确定可控因素对研究结果影响力的大小  \n",
    "\n",
    "参考文章：https://www.jianshu.com/p/f5f54a39cb19"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[+]F1 score :  0.9205265724037055\n",
      "[+]acc score:  0.9185\n"
     ]
    }
   ],
   "source": [
    "from sklearn.feature_selection import SelectKBest\n",
    "from sklearn.feature_selection import f_classif\n",
    "\n",
    "selector = SelectKBest(f_classif, k=25).fit(X_train, y_train)  # 保留25个特征\n",
    "X_new_train = selector.transform(X_train)\n",
    "X_new_test = selector.transform(X_test)\n",
    "clf = LogisticRegression(random_state=seed)\n",
    "clf.fit(X_new_train, y_train)\n",
    "get_socre(y_test, clf.predict(X_new_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[+]F1 score :  0.922552362396493\n",
      "[+]acc score:  0.9205\n"
     ]
    }
   ],
   "source": [
    "selector = SelectKBest(f_classif, k=35).fit(X_train, y_train)  # 保留35个特征\n",
    "X_new_train = selector.transform(X_train)\n",
    "X_new_test = selector.transform(X_test)\n",
    "clf = LogisticRegression(random_state=seed)\n",
    "clf.fit(X_new_train, y_train)\n",
    "get_socre(y_test, clf.predict(X_new_test))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 互信息\n",
    "互信息用于衡量两个变量之间的相关性，如果两个变量之间相互独立，那么互信息为0  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[+]F1 score :  0.919921875\n",
      "[+]acc score:  0.918\n"
     ]
    }
   ],
   "source": [
    "from sklearn.feature_selection import SelectKBest\n",
    "from sklearn.feature_selection import mutual_info_classif\n",
    "\n",
    "selector = SelectKBest(mutual_info_classif, k=25).fit(X_train, y_train)  # 保留25个特征\n",
    "X_new_train = selector.transform(X_train)\n",
    "X_new_test = selector.transform(X_test)\n",
    "clf = LogisticRegression(random_state=seed)\n",
    "clf.fit(X_new_train, y_train)\n",
    "get_socre(y_test, clf.predict(X_new_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[+]F1 score :  0.9210526315789473\n",
      "[+]acc score:  0.919\n"
     ]
    }
   ],
   "source": [
    "selector = SelectKBest(mutual_info_classif, k=35).fit(X_train, y_train)  # 保留35个特征\n",
    "X_new_train = selector.transform(X_train)\n",
    "X_new_test = selector.transform(X_test)\n",
    "clf = LogisticRegression(random_state=seed)\n",
    "clf.fit(X_new_train, y_train)\n",
    "get_socre(y_test, clf.predict(X_new_test))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 模型选择特征"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 递归特征消除（RFE）\n",
    "递归特征消除的主要思想是反复构建模型，每次从当前的一组特征中删除最不重要的特征，然后对该过程进行递归重复，直到最终达到所需的特征数量"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[False  True False  True False  True  True False False False False False\n",
      " False False False  True  True False False False False  True False  True\n",
      "  True  True  True  True  True False  True  True False  True  True  True\n",
      "  True False False  True False False  True False  True  True  True False\n",
      "  True False]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([17,  1, 20,  1, 26,  1,  1,  9, 21, 11, 10, 24,  6, 14, 18,  1,  1,\n",
       "        7,  2, 12,  3,  1, 25,  1,  1,  1,  1,  1,  1, 23,  1,  1, 16,  1,\n",
       "        1,  1,  1,  5,  8,  1, 22, 13,  1,  4,  1,  1,  1, 15,  1, 19])"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.feature_selection import RFE\n",
    "\n",
    "clf = LogisticRegression(random_state=seed)\n",
    "\n",
    "selector = RFE(clf, 25)   # 保留25个特征\n",
    "selector = selector.fit(X_train, y_train)\n",
    "print(selector.support_)  # 保留的特征为True\n",
    "selector.ranking_         # 保留的特征等级为1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[+]F1 score :  0.9215017064846417\n",
      "[+]acc score:  0.9195\n"
     ]
    }
   ],
   "source": [
    "clf = LogisticRegression(random_state=seed)\n",
    "clf.fit(X_train[:, selector.support_], y_train)\n",
    "get_socre(y_test, clf.predict(X_test[:, selector.support_]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 随机森林\n",
    "参考文章：https://www.jianshu.com/p/8985bc8e4a12"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([44,  7, 22, 38, 14, 47, 23,  1, 42, 29, 17, 16,  4, 43, 48,  3, 45,\n",
       "       30,  9, 15, 49, 33, 18, 40,  2, 46,  8, 25, 19, 11, 36,  5, 34, 27,\n",
       "        6], dtype=int64)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"\"\"\n",
    "根据 feature_importances_ 属性选择特征\n",
    "\"\"\"\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "rf = RandomForestClassifier(random_state=seed)\n",
    "rf.fit(X_train, y_train)\n",
    "feature_importances = rf.feature_importances_\n",
    "feature_importances_index = feature_importances.argsort()  # 元素的索引，越往后，索引对应的元素越大\n",
    "keep_features = feature_importances_index[-35:]            # 保留35个重要特征\n",
    "keep_features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[+]F1 score :  0.9211943220753794\n",
      "[+]acc score:  0.9195\n"
     ]
    }
   ],
   "source": [
    "clf = LogisticRegression(random_state=seed)\n",
    "clf.fit(X_train[:, keep_features], y_train)\n",
    "get_socre(y_test, clf.predict(X_test[:, keep_features]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[+]F1 score :  0.9202153695545767\n",
      "[+]acc score:  0.9185\n"
     ]
    }
   ],
   "source": [
    "\"\"\"\n",
    "可以借助SelectFromModel\n",
    "SelectFromModel是一个较不健壮的解决方案。 它只是根据计算出的阈值删除不那么重要的功能（不涉及优化迭代过程）。\n",
    "传入的模型必须具有 coef_ 属性或 feature_importances_ 属性\n",
    "\"\"\"\n",
    "from sklearn.feature_selection import SelectFromModel\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "rf = RandomForestClassifier(random_state=seed)\n",
    "selector = SelectFromModel(rf).fit(X_train, y_train)\n",
    "\n",
    "X_new_train = selector.transform(X_train)\n",
    "X_new_test = selector.transform(X_test)\n",
    "clf = LogisticRegression(random_state=seed)\n",
    "clf.fit(X_new_train, y_train)\n",
    "get_socre(y_test, clf.predict(X_new_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(8000, 4)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_new_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.02\n",
      "0.02\n"
     ]
    }
   ],
   "source": [
    "\"\"\"\n",
    "SelectFromModel 根据 threshold 模型的 coef_ 属性或 feature_importances_ 属性来保留特征\n",
    "默认将 均值 作为其阈值\n",
    "\"\"\"\n",
    "print(feature_importances.mean())\n",
    "print(selector.threshold_)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 极度随机树\n",
    "ExtRa Trees是Extremely Randomized Trees的缩写，意思就是极度随机树。这是一种组合方法，与其说像决策树，实际上它更像随机森林。\n",
    "\n",
    "与随机森林的相同点：\n",
    "1. bootstrap了样本。\n",
    "2. 随机选取了部分特征，来构造一个棵树。\n",
    "\n",
    "与随机森林的不同点：\n",
    "1. 每棵决策树选择划分点的方式不同。对于普通决策树，每个特征都是根据某个标准(信息增益或者gini不纯)去进行划分。而对于extra trees中的决策树，划分点的选择更为随机，随机选择一个划分点；然后再按照评判标准选择一个特征。\n",
    "\n",
    "参考文章：http://sofasofa.io/forum_main_post.php?postid=1000765"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[+]F1 score :  0.9190243902439025\n",
      "[+]acc score:  0.917\n"
     ]
    }
   ],
   "source": [
    "from sklearn.ensemble import ExtraTreesClassifier\n",
    "etclf = ExtraTreesClassifier(random_state=seed)\n",
    "etclf.fit(X_train, y_train)\n",
    "feature_importances = etclf.feature_importances_\n",
    "feature_importances_index = feature_importances.argsort()  # 元素的索引，越往后，索引对应的元素越大\n",
    "keep_features = feature_importances_index[-35:]            # 保留35个重要特征\n",
    "clf = LogisticRegression(random_state=seed)\n",
    "clf.fit(X_train[:, keep_features], y_train)\n",
    "get_socre(y_test, clf.predict(X_test[:, keep_features]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 相关性分析"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "使用皮尔逊相关系数检查两个变量之间变化趋势的方向以及程度，值范围-1到+1，0表示两个变量不相关，正值表示正相关，负值表示负相关，值越大相关性越强"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "27    0.375743\n",
       "34    0.456700\n",
       "6     0.744042\n",
       "y     1.000000\n",
       "Name: y, dtype: float64"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"\"\"\n",
    "找出与类别最相关的特征\n",
    "\"\"\"\n",
    "df = pd.DataFrame(X_train)\n",
    "df['y'] = y_train\n",
    "corr= df.corr()\n",
    "corr_y = abs(corr[\"y\"])\n",
    "highest_corr = corr_y[corr_y > 0.1]       # 只看大于0.1的\n",
    "highest_corr.sort_values(ascending=True)  # 发现只有3个特征与标签最相关"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "keep_features = highest_corr.sort_values(ascending=True).index[:-1]  # 去掉y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[+]F1 score :  0.9197651663405088\n",
      "[+]acc score:  0.918\n"
     ]
    }
   ],
   "source": [
    "clf = LogisticRegression(random_state=seed)\n",
    "clf.fit(df[keep_features], y_train)\n",
    "get_socre(y_test, clf.predict(X_test[:, [27,34,6]]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x118e3f30f48>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 960x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\"\"\"\n",
    "去除相关度高（冗余）的特征，特征较少时可以用画图的方式\n",
    "我们选择与标签最相关的10特征\n",
    "\"\"\"\n",
    "df_2 = df[corr_y.sort_values(ascending=True)[-11:-1].index]\n",
    "\n",
    "figure(figsize=(12, 10), dpi=80, facecolor='w', edgecolor='k')\n",
    "corr_2 = df_2.corr()\n",
    "sns.heatmap(corr_2, annot=True, fmt=\".2g\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10个特征\n",
      "[+]F1 score :  0.9184968277208394\n",
      "[+]acc score:  0.9165\n",
      "9个特征\n",
      "[+]F1 score :  0.527244421380384\n",
      "[+]acc score:  0.5445\n"
     ]
    }
   ],
   "source": [
    "\"\"\"\n",
    "特征6 和 特征34 高度相关，特征27 和 特征34 也比较相关，我们去掉特征34\n",
    "特征6 和 特征27 也比较为相关，可以尝试去除特征6\n",
    "\"\"\"\n",
    "print(\"10个特征\")\n",
    "keep_features = corr_y.sort_values(ascending=True)[-11:-1].index  # 去掉y\n",
    "keep_features = keep_features.values.astype(np.int64)              # 变为int类型\n",
    "\n",
    "clf = LogisticRegression(random_state=seed)\n",
    "clf.fit(df[keep_features], y_train)\n",
    "get_socre(y_test, clf.predict(X_test[:, keep_features]))\n",
    "\n",
    "print(\"9个特征\")\n",
    "keep_features = np.delete(keep_features, [8, 9], axis=0)  # 34在第8个位置，6在第9位置\n",
    "clf = LogisticRegression(random_state=seed)\n",
    "clf.fit(df[keep_features], y_train)\n",
    "get_socre(y_test, clf.predict(X_test[:, keep_features]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## L1正则\n",
    "L1正则可以让模型的解更加稀疏，相当于做了特征选择"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[+]F1 score :  0.9195514383227693\n",
      "[+]acc score:  0.9175\n"
     ]
    }
   ],
   "source": [
    "\"\"\"\n",
    "直接使用L1正则作为惩罚项\n",
    "\"\"\"\n",
    "clf = LogisticRegression(random_state=seed, solver='saga', penalty='l1')  # 大多默认使用L2正则，默认的solver不支持L1正则\n",
    "clf.fit(X_train, y_train)\n",
    "get_socre(y_test, clf.predict(X_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-9.51245569e-03,  7.32183085e-02,  4.83180957e-03,\n",
       "         4.30733017e-02,  0.00000000e+00, -4.66811406e-01,\n",
       "         3.05351025e+00,  1.92057468e-02, -3.02207220e-03,\n",
       "         1.71185771e-02, -1.79497466e-02,  1.50594500e-03,\n",
       "        -2.35858507e-02, -1.56778248e-02, -8.13824619e-03,\n",
       "         4.75994406e-02,  4.40293302e-02, -2.11428813e-02,\n",
       "        -2.83840948e-02, -1.80366925e-02, -2.66550994e-02,\n",
       "        -6.08503882e-02,  0.00000000e+00, -4.66328823e-02,\n",
       "        -3.07754761e-02,  3.51362465e-02,  3.12259683e-02,\n",
       "        -3.95710526e-01,  4.51142572e-02, -2.08816937e-03,\n",
       "         3.92244686e-02,  6.06499896e-02,  9.42245903e-03,\n",
       "         5.29337953e-02,  0.00000000e+00, -3.15209645e-02,\n",
       "        -3.49550033e-02, -2.57010991e-02,  1.97250041e-02,\n",
       "         5.36825749e-02, -2.26434646e-03, -1.59557488e-02,\n",
       "         4.38363779e-02,  2.59519955e-02,  4.01781929e-02,\n",
       "         6.45112216e-02,  9.37199646e-02, -9.79968706e-03,\n",
       "        -3.26383784e-02, -6.34598643e-03]])"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 可以看到有3个0分量，相当于只用了47个特征\n",
    "clf.coef_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[+]F1 score :  0.9185763042418332\n",
      "[+]acc score:  0.9165\n"
     ]
    }
   ],
   "source": [
    "clf = LogisticRegression(random_state=seed, solver='saga', penalty='l2')  # 使用L2正则作为对照\n",
    "clf.fit(X_train, y_train)\n",
    "get_socre(y_test, clf.predict(X_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-1.08921100e-02,  7.48467989e-02,  6.26940633e-03,\n",
       "         4.46747399e-02, -1.25052588e-03, -6.61374591e-01,\n",
       "         2.53943109e+00,  2.08043212e-02, -4.31963267e-03,\n",
       "         1.86689835e-02, -1.94129903e-02,  2.97867411e-03,\n",
       "        -2.50700197e-02, -1.68370235e-02, -9.64746755e-03,\n",
       "         4.90329505e-02,  4.52764233e-02, -2.31655798e-02,\n",
       "        -2.98308541e-02, -1.95454371e-02, -2.79398592e-02,\n",
       "        -6.20594534e-02,  1.78168377e-03, -4.82334054e-02,\n",
       "        -3.20009028e-02,  3.67419561e-02,  3.30389727e-02,\n",
       "        -8.99388533e-01,  4.65904247e-02, -3.49941446e-03,\n",
       "         4.08514183e-02,  6.22428361e-02,  1.11832246e-02,\n",
       "         5.44403237e-02, -8.42321704e-01, -3.28235687e-02,\n",
       "        -3.61683319e-02, -2.72109912e-02,  2.12362903e-02,\n",
       "         5.54439459e-02, -3.76669583e-03, -1.76304242e-02,\n",
       "         4.52219803e-02,  2.75392075e-02,  4.15925722e-02,\n",
       "         6.57780799e-02,  9.51320196e-02, -1.14884322e-02,\n",
       "        -3.39432636e-02, -7.98007378e-03]])"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# L1正则的解比L2正则的稀疏\n",
    "# 这里使用L2正则就没有0分量\n",
    "clf.coef_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[+]F1 score :  0.9190243902439025\n",
      "[+]acc score:  0.917\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([[-1.09291020e-02,  7.51816545e-02,  6.21075872e-03,\n",
       "         4.49933765e-02, -1.32496087e-03, -6.65089007e-01,\n",
       "         2.55168233e+00,  2.09757028e-02, -4.38317029e-03,\n",
       "         1.89730198e-02, -1.95811006e-02,  2.88288167e-03,\n",
       "        -2.52665511e-02, -1.69769621e-02, -9.67406704e-03,\n",
       "         4.94252961e-02,  4.56604886e-02, -2.33097291e-02,\n",
       "        -3.00064236e-02, -1.96306150e-02, -2.81423660e-02,\n",
       "        -6.24040447e-02,  1.72615918e-03, -4.85314639e-02,\n",
       "        -3.20968120e-02,  3.69763984e-02,  3.31646931e-02,\n",
       "        -9.03969915e-01,  4.68198081e-02, -3.50570308e-03,\n",
       "         4.09494008e-02,  6.25825239e-02,  1.14700497e-02,\n",
       "         5.46637854e-02, -8.46118899e-01, -3.30439900e-02,\n",
       "        -3.63456020e-02, -2.73710935e-02,  2.14092321e-02,\n",
       "         5.58930136e-02, -3.71554623e-03, -1.77930800e-02,\n",
       "         4.53222228e-02,  2.77130824e-02,  4.17473178e-02,\n",
       "         6.62082406e-02,  9.57603189e-02, -1.16069695e-02,\n",
       "        -3.42077415e-02, -8.11364465e-03]])"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf = LogisticRegression(random_state=seed, solver='saga', penalty='none')  # 不使用惩罚项作为对照\n",
    "clf.fit(X_train, y_train)\n",
    "get_socre(y_test, clf.predict(X_test))\n",
    "clf.coef_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "消除特征数： 3\n",
      "保留特征数： 47\n"
     ]
    }
   ],
   "source": [
    "\"\"\"\n",
    "L1正则作特征选择，画图看重要性（系数大小）\n",
    "\"\"\"\n",
    "from sklearn.linear_model import LassoCV\n",
    "\n",
    "clf = LogisticRegression(random_state=seed, solver='saga', penalty='l1')\n",
    "clf.fit(X_train, y_train)\n",
    "\n",
    "print(\"消除特征数：\", len(clf.coef_[clf.coef_==0]))\n",
    "print(\"保留特征数：\", len(clf.coef_[clf.coef_!=0])) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, '使用Lasso获得特征的重要性（不为0的系数）')"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 960x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "model_coef = pd.Series(clf.coef_.reshape(-1))\n",
    "figure(num=None, figsize=(12, 10), dpi=80, facecolor='w', edgecolor='k')\n",
    "top_coef = model_coef.sort_values()\n",
    "top_coef[top_coef != 0].plot(kind = \"barh\")\n",
    "plt.title(\"使用Lasso获得特征的重要性（不为0的系数）\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# PCA降维\n",
    "使用奇异值分解将数据投影到较低的k维空间，这k维特征被称为主成份  \n",
    "\n",
    "参考文章：https://www.jianshu.com/p/bcd196497d94  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.16078459373927048, 0.2652297979042278, 0.36983423462781223, 0.4707614443558456, 0.5699266896524299, 0.669319129162304, 0.7667601514569581, 0.8607271129195249, 0.9489175018749659]\n"
     ]
    }
   ],
   "source": [
    "# 画图看降到多少维合适\n",
    "from sklearn.decomposition import PCA\n",
    "candidate_components = range(5, 50, 5)  # 有50个特征，我们最低取5维，并以5为步长递增\n",
    "explained_ratios = []\n",
    "for c in candidate_components:\n",
    "    pca = PCA(n_components=c)\n",
    "    pca.fit(X_train)\n",
    "    explained_ratios.append(np.sum(pca.explained_variance_ratio_))\n",
    "print(explained_ratios)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([<matplotlib.axis.XTick at 0x118e61053c8>,\n",
       "  <matplotlib.axis.XTick at 0x118e6106088>,\n",
       "  <matplotlib.axis.XTick at 0x118e3e26208>,\n",
       "  <matplotlib.axis.XTick at 0x118e612f0c8>,\n",
       "  <matplotlib.axis.XTick at 0x118e612fac8>,\n",
       "  <matplotlib.axis.XTick at 0x118e61345c8>,\n",
       "  <matplotlib.axis.XTick at 0x118e6138188>,\n",
       "  <matplotlib.axis.XTick at 0x118e6138bc8>,\n",
       "  <matplotlib.axis.XTick at 0x118e613c6c8>,\n",
       "  <matplotlib.axis.XTick at 0x118e613f248>],\n",
       " <a list of 10 Text xticklabel objects>)"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1440x864 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 6), dpi=144)\n",
    "plt.grid()\n",
    "plt.plot(candidate_components, explained_ratios)\n",
    "plt.xlabel('Number of PCA Components')\n",
    "plt.ylabel('Explained Variance Ratio')\n",
    "plt.title('Explained variance ratio for PCA')\n",
    "plt.yticks(np.arange(0.5, 1, 0.1))\n",
    "plt.xticks(np.arange(0, 50, 5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[+]F1 score :  0.919921875\n",
      "[+]acc score:  0.918\n"
     ]
    }
   ],
   "source": [
    "\"\"\"\n",
    "发现图是线性的，没有明显拐点，尽量选择方差百分比高的主成分数\n",
    "40占所有特征的方差百分比为0.8607271129195249\n",
    "\"\"\"\n",
    "pca = PCA(n_components=40)\n",
    "X_new_train = pca.fit_transform(X_train)\n",
    "X_new_test = pca.transform(X_test)\n",
    "clf = LogisticRegression(random_state=seed)\n",
    "clf.fit(X_new_train, y_train)\n",
    "get_socre(y_test, clf.predict(X_new_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[+]F1 score :  0.9208984375\n",
      "[+]acc score:  0.919\n"
     ]
    }
   ],
   "source": [
    "\"\"\"\n",
    "45占所有特征的方差百分比为0.9489175018749659\n",
    "\"\"\"\n",
    "pca = PCA(n_components=45)\n",
    "X_new_train = pca.fit_transform(X_train)\n",
    "X_new_test = pca.transform(X_test)\n",
    "clf = LogisticRegression(random_state=seed)\n",
    "clf.fit(X_new_train, y_train)\n",
    "get_socre(y_test, clf.predict(X_new_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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